Implicit profile for use with recommendation engine and/or question router

ABSTRACT

Methods and systems for creating an implicit profile for use by a recommendation engine or a question router is provided. User behavior on at least one of one or more electronic devices and an electronic communications network is tracked. User-related information relating to the user behavior is analyzed to extract or derive key words therefrom. The key words are stored in a profiles database as the implicit profile and used by the recommendation engine or question router to characterize user interests, expertise, and skills when matching a request from a querying user to a potential user or group of users having the relevant background to respond to the request.

CROSS REFERENCE

This application is a continuation-in-part of commonly owned, co-pendingU.S. patent application Ser. No. 13/532,936 filed on Jun. 26, 2012,which is a continuation-in-part of U.S. patent application Ser. No.12/592,799 filed on Dec. 2, 2009 (now U.S. Pat. No. 8,244,674), andclaims the benefit of U.S. Provisional Patent Application No. 61/601,085filed on Feb. 21, 2012, each of which is incorporated herein byreference in their entirety and for all purposes.

BACKGROUND OF THE INVENTION

The present invention relates to an implicit profile for use with arecommendation engine or a question router. The recommendation engineand question router are part of a peer directory which provides for theonline location of peers that can be considered experts in a particularbusiness or endeavor. Once qualified peers are located, connections tosuch peers can be requested for project, product and implementationadvice and information, and the like. The implicit profile of thepresent invention is an extension of the profiles disclosed for use inthe question router and the peer directory disclosed in theabove-identified parent applications and enables an increase in thequestion and answer rates in a peer forum system. The question routeraccomplishes this result by automatically sending the questions to themost appropriate subject matter experts in the peer directory throughmatching of question key words with key words from the implicit andexplicit user profiles.

Various tools for arranging business introductions are known in the art.For example, J. Greenfield U.S. Patent Publication No. 2009/0018851discloses a network that uses registration information of multipleparties along with a matching function to match two parties that have abusiness contact that both parties would benefit from if the partieswere introduced. Procedures are provided to notify the parties of apotential match, and to facilitate communication between the parties ifthe introduction is accepted by the parties.

U.S. Pat. No. 7,454,433 to Ebert discloses a system for providingadaptive virtual communities. By determining a technical or businesscontext of a particular user, the system is able to match that user withother users who are likely to be able to assist the user within thatcontext.

U.S. Pat. No. 7,035,838 to Nelson et al. discloses methods and systemsfor organizing information stored within a computer network-basedsystem. Documentation relating to a plurality of topics and a list ofexperts on a plurality of topics is stored in a centralized database. Auser interface enables a user to search the database for a specific itemof information by at least one of a work function, a functional categoryand a community.

Prior art systems, such as those referenced above, generally provide toomany potential matches between a requester and available contacts. Therequester will then have to sort through these many potential matches toattempt to find a match that will be most relevant. Such systems canwaste the requester's time and may not result in the best match beingfound, since the requester may settle for a less relevant match insteadof carefully considering each of the many potential matches presented.Moreover, once a match is selected by a requester, the individualassociated with that match may not respond to a request by the requesterto communicate. This can waste more time, as the requester may waitseveral days to hear back from the match, only to find that no responseis ever received. The requester will then have to find another match,with no assurance that the individual associated with the new match willbe likely to respond to a request to communicate.

The present invention addresses the lack of a healthy question andanswer rate in a web based community forum. A good question and answer(“Q&A”) rate is necessary to keep the community dynamic and healthy.Typically community managers play a big role in keeping the communitydynamic by answering questions or facilitating the answering ofquestions. This can be a very manual and time intensive process. Itwould be advantageous to increase the question and answer rates in acommunity forum in an automated fashion, thereby cutting down the amountof manual intervention.

In an improved system contemplated by the present invention, clients(peers) consist of a highly respected set of experts in their areas.Peers are encouraged to post their questions/thoughts on the communityQ&A forums. A system in accordance with the present invention can thenbe used to help increase the engagement of community members in theforums and get their questions answered.

It would be advantageous to provide improved apparatus and methods forrouting questions posted by peers to other peers in the community thatcan best assist a user in solving a business or technical problem. Itwould be further advantageous if such apparatus and methods wouldprovide more relevant matches to the requester, to increase thelikelihood that a helpful peer will respond to the questions quickly andefficiently. It would be still further advantageous if potential matchespresented to the requester comprise peers that are more likely than notto respond to a request to communicate with the requester. Inparticular, it would be advantageous to increase the question and answerrates in a community forum in an automated fashion, thereby cutting downthe amount of manual intervention.

The present invention provides an implicit profile for use with arecommendation engine or a question router that provides theaforementioned and other advantages.

SUMMARY

In accordance with the invention, a peer directory system is provided.The system is implemented on a digital computer network. A userinterface enables explicit profile information of a user to be enteredand stored in a profiles database. A search engine is adapted to appendtags to the explicit profile information. The search engine cancomprise, for example, a computer processor and software to implement asearch function. A search index is associated with the search engine forstoring tagged user profile information in an indexed form. A peerrelevancy algorithm is associated with the search engine to search forcandidate peers among the indexed user profile information stored in thesearch index. The peer relevancy algorithm assigns weights to candidatepeers based on different categories of the indexed user profileinformation, and selects peer matches based on the assigned weights.

In an illustrated embodiment, a first weight is assigned to candidatepeers who have a best initiative match with a user searching for peers.The “initiative” can be, for example, a project or venture that the useris currently working on for an enterprise such as an employer. A secondweight is assigned to candidate peers who have a best vendor/productmatch with the user searching for peers. A third weight is assigned tocandidate peers who have a best primary operating system (OS) match(e.g., Windows, Mac OS X, SunOS, Linux, Unix, etc.) with the usersearching for peers. A fourth weight is assigned to candidate peers whohave a best industry match with the user searching for peers. A fifthweight is assigned to candidate peers who have a best firm size match(e.g., size of employer by number of employees, sales revenue, etc.)with the user searching for peers.

The first, second, third, fourth and fifth weights can be summed acrossall tags for the candidate peers in order to provide a composite weightfor each candidate peer. The candidate peers can then be sorted by theircomposite weights.

In a preferred embodiment of the invention, the search index storesinformation indicative of past connection responses for candidate peers.Based on this information, the peer relevancy algorithm provides either(a) a negative bias to candidate peers that have poor past connectionresponses, or (b) a positive bias to candidate peers that have good pastconnection responses.

The user interface may comprise a display processor for providingdisplay information indicative of best matched peers and allowinginformation about the best matched peers to be viewed and filtered by auser searching for peers. The user interface may also comprise anysuitable type of data entry means such as a keyboard, mouse, touchscreen, or the like.

The peer relevancy algorithm can be implemented such that it isresponsive to a request entered via the user interface to select a peermatch for a requester. In such an embodiment, the algorithm will returnpeer matches to the requester via the user interface. The user interfacecan be implemented to enable the requester to request connection to oneor more peers identified by the peer matches. A communicationsprocessor, responsive to a peer connection requested by the requester,may be provided for (i) generating a connection request message to theapplicable peer, (ii) receiving a reply from said applicable peer, (iii)if the applicable peer accepts the connection, sending a connectionacceptance to the requester with contact information for the applicablepeer, and (iv) if the applicable peer fails to accept the connection,sending a connection rejection to said requester.

In a preferred embodiment, the connection request message discloses atleast one of the requester's company, industry, role or a personalmessage from the requester without disclosing the identity of therequester. Contact information for the requester is disclosed to theapplicable peer only if the connection is accepted.

Various additional features of the invention include the ability of theuser interface to allow a user to filter peer matches by at least one ofindustry, firm size, country, job role, vendor and product/servicecategory. The weights assigned to the various candidate peers based ondifferent categories of the indexed user profile information can beconfigurable to allow, e.g., for the tuning of the weights due topresent or future circumstances. The negative and positive biasesprovided to candidate peers based on their past connection responsehistory can also be configurable, e.g., to increase or decrease thesignificance of the bias in choosing peer matches for presentation(e.g., display) to a requester.

A method is also disclosed for connecting peers having common interests.The method enables explicit profile information to be collected from auser. Tags are appended to the explicit profile information. Tagged userprofile information is stored in a profiles database in an indexed form.The profiles database is searched to identify candidate peers inresponse to a request for a peer match. The identification of candidatepeers is based on correlations between a requester's user profileinformation and user profile information for the candidate peers.Weights are assigned to the candidate peers, and peer matches areselected based on the assigned weights.

In an illustrated embodiment, the weights assigned to candidate peersare based on at least one of best initiative match, best vendor match,best product match, best primary operating system (OS) match, bestindustry match and best firm size match. The weights assigned tocandidate peers are summed for each such peer. The candidate peers aresorted by their composite weights.

Information indicative of past connection responses for candidate peerscan be maintained. Based on this information, a negative bias can beprovided to candidate peers that have poor past connection responses,and a positive bias can be provided to candidate peers that have goodpast connection responses.

In a further embodiment of the invention, individual clients have accessto online peer forum systems. The system is implemented on a digitalcomputer network and includes a user interface operatively associatedwith a digital computer for enabling questions to be input via thedigital computer network. Access to such peer forum systems is generallyrestricted to a highly qualified set of individuals. Users have toregister, provide explicit profile information and login to access theforums. Users can ask questions on the forum and get relevant answersfrom their peers. A question router, which operates via a questionrouter algorithm, is associated with the computer network to enablehigher answer rates for questions posed by users. The question routeralgorithm is completely automated and routes input questions to the mostrelevant peers, thereby increasing the answer rates with no manualintervention. This also helps indirectly to increase the question ratessince peers find their questions answered and thereby feel confidentabout posing more questions.

A forums database is associated with the question router and capturesall the questions and answers input by clients. At any given point intime, questions or answers can be looked up in this database. A seconddatabase pulls all open questions from the forums database into its ownstorage. Open questions are questions for which no reply has been madeor no answer has been given. The system can also be designed to pull allquestions and replies on a periodic basis. A peer search module pullsthe open questions from the second database to find peers who can answerthe questions. The peer search module consists of a recommendationengine and peer profile database modules.

The recommendation engine is able to find peers (e.g., experts)qualified to answer the questions. This is done using a combination ofcollaborative and cluster filtering algorithms. The recommendationengine may take into consideration both explicit and implicit profilesof a peer to figure out the peer's subject matter expertise. If thepeer's subject matter expertise is the same as the open question, thenhe becomes a candidate to answer the open question. The peer's subjectmatter profile is further strengthened by his propensity to answerquestions on the peer forum system and his expertise as demonstrated inthe peer forum systems.

The peer profile database module acts as input to the recommendationengine so that the qualified peer matches can be found. This databasestores the user's explicit and implicit profile. The explicit profilecomprises information that generally defines the user based on theuser's direct input into the system. This is usually derived fromregistration forms where the user has input his industry experience, jobtitles and duty descriptions, size of company, company name, projects heis working on, vendors he is working with, etc. The implicit profile ofa user is based on the user's tracked behavior on one or more electronicdevices and/or on the web site used to access the inventive system. Theimplicit profile is described in detail below.

After receiving open questions into the peer search module and receivingthe peer recommendations from the recommendation engine, “throttle”rules are used to determine if each peer in a candidate set of peers canbe sent an email or other type of electronic message encouraging them toanswer the open questions. There might be rules that limit the number ofmessages that can be sent to a candidate, such as “only send threeemails per person per week.” The peers who can get past the throttlerules become eligible to be sent messages, requesting them to answer thequestions. For example, an email delivery system may send emails to thepeers requesting them to answer the open questions. The email deliverysystem consists of email templates and email sending systems. A touchdatabase captures all the sent emails. This data can be used in thefuture to figure out the number of emails sent to users and to adjustand/or derive email throttling rules.

Peer experts open the emails received from the email delivery system andfind questions they can answer. The email will contain, for example,links which will take the peer experts to the open question where theycan comment on or answer the question.

In a further example embodiment of the present invention, a method forcreating an implicit profile for use by a recommendation engine or aquestion router is provided. An example embodiment of an automatedmethod for creating an implicit profile for use by at least one of arecommendation engine or a question router comprises tracking userbehavior on at least one of one or more electronic devices and anelectronic communications network, analyzing user-related informationrelating to the user behavior to extract or derive key words therefromwhich are used to characterize user interests, expertise, and skills,and storing the key words in a profiles database as the implicitprofile. The key words are used by the recommendation engine or questionrouter when matching a request from a querying user to a potential useror group of users having the relevant background to respond to therequest.

The user-related information may comprise at least one of search termsused, documents read, documents opened, documents printed, documentssaved, documents created, documents edited, documents commented on,annotations entered on documents, highlighted terms in documents,websites visited, webpages viewed, Internet searches conducted, ratingsprovided by the user on documents, products or services, user-createdproduct or service reviews, multi-media items played, social forumthreads opened, social forum threads participated in, people profilesopened, user items shared on the electronic communications network,shared items of others on the electronic communications network accessedby the user, user created content, online events, seminars, trainingcourses, or webinars attended, in-person or online events, seminars, ortraining courses registered for, news or information feeds set up on theelectronic communications network, emails written, emails received,blogs read, blog posts entered, software applications installed,computer hardware installed, software updates downloaded, one or more ofalerts, follows, and likes set up on the electronic communicationsnetwork, and similar information.

In addition, the user-related information may be obtained from useractivity on a web site used to access the recommendation engine or thequestion router. For example, the user-related information may compriseat least one of questions answered, answers provided, information andmaterials reviewed in answering questions, number of questions answeredin a subject area, percentage of questions accepted for response,subject matter expertise based on answers submitted, number of timesrecommended as an expert in a subject area, user frequency of logging into the web site, user preference for type of digital network, userpreference for type of electronic device, user preferred informationchannels including one or more of reading or printing of documents, useof multimedia, and interacting socially in forums, user open andclick-through rates for relevant emails, and similar information.

In addition, the user-related information may comprise at least one ofuser requests or user responses via the web site used to access therecommendation engine or the question router.

Optionally, the user-related information may also comprise user requestssubmitted to the recommendation engine or the question router via onlineforms, emails, or computer applications that are recorded in a requestdatabase to find at least one of experts, analysts, or peers, or toreceive requested information or materials.

Further, the user-related information may comprise user requestssubmitted to the recommendation engine or the question router or otherlinked web-based tools via online forms, emails, or computerapplications that are recorded in a request database for at least one ofvendor proposals, product demonstrations, price quotes, and the like.

The user-related information may be obtained from electronic imprintsfrom user interaction with the one or more electronic devices that arelogged in a database. The one or more electronic devices may comprise atleast one of a computer, a tablet computer, a laptop, a smartphone, anInternet enabled device, and the like.

The electronic imprints may be created by at least one of using a webinterface, using a web browser, using a mobile application, using acomputer application or program, sending or receiving an email, loggingor recording a telephone call or voice message, manipulation of aself-reporting electronic system, downloading or installing one or moreof programs, applications, documents, multimedia content, music, andsoftware updates, and the like.

In one example embodiment, weightings may be assigned and stored witheach of the key words in the profiles database. The weightings may beassigned based on one or more of relevancy calculations of theuser-related information, an estimate of accuracy of the user-relatedinformation, type of the user-related information, source of theuser-related information, amount of each type of the user-relatedinformation, time spent by user on each type or item of user-relatedinformation, recency of the user-related information, relation of theuser-related information to current key words in the implicit profile,relation of the user-related information to information in an explicitprofile of the user, and the like.

The tracking may comprise at least one of storing data from the userbehavior at the time of the user behavior, searching the one or moreelectronic devices for data relating to the user behavior, recordinguser interaction on the one or more electronic devices, and the like.

The tracking may be enabled via a crawling or searching applicationrunning on the one or more electronic devices for searching variouscomputer applications or storage locations on the one or moreelectronics device for obtaining the user-related information.

The various computer applications may comprise one or more of a wordprocessing application, a web browser, an electronic calendar, an emailprogram, spreadsheet applications, social media applications, messagingapplications, content editing, highlighting, and annotating programs,and the like.

The storage locations may comprise at least one of hard drive locations,file folders, document folders, web browser cookie folders, emailfolders, databases, spreadsheet folders, shared folders, networkedfolders, music folders, software application folders, media files, filedirectories, social directories, activity logs, and the like.

The key words may be extracted or derived from the user-relatedinformation by applying at least one of lexical analysis, metadataanalysis, natural language processing analysis, or similar processingtechniques or combinations thereof.

The method may further comprise storing an explicit profile for the userin the profiles database together with the implicit profile for use byat least one of the recommendation engine and the question router. Theexplicit profile may comprise profile data obtained by direct input fromthe user.

A plurality of the implicit and explicit profiles may be stored for eachof a corresponding plurality of respective users in the profilesdatabase. The implicit profile of the user may be matched with theexplicit profile of the user for use in processing recommendationrequests or information requests.

In addition, the key words, values, key word weightings, and otherinformation stored in the matching explicit and implicit profiles of theuser may be merged to create a merged profile for use by at least one ofthe recommendation engine and the question router.

The question router may be adapted to automatically route a questionfrom a querying user to one or more of the other users on the electroniccommunications network based on a matching of key words obtained fromthe question with at least the implicit profiles of the one or moreother users.

The recommendation engine may be adapted to accept a recommendationrequest for an expert from a querying user and recommending one or moreof the other users on the electronic communications network as an expertbased on a matching of key words obtained from the recommendationrequest with at least the implicit profiles of the one or more otherusers.

The method may also comprise providing user feedback on relevancy of atleast one of a recommendation request from the recommendation engine, aresponse to the recommendation request from a recommended peer, aquestion from the question router, and a response to the question fromthe recommended peer to the user's expertise, and storing of thefeedback. The user's feedback on relevancy may be used to adjust keyword weightings in the implicit profile of the user and/or therecommended peer.

A system for creating an implicit profile for use by at least one of arecommendation engine or a question router is also provided inaccordance with the present invention. An example embodiment of such asystem may comprise an electronic communications network and one or moreelectronic devices for each user in communication with the electroniccommunications network. A software application running on each of theelectronic devices is adapted for tracking user behavior on at least oneof the one or more electronic devices and the electronic communicationsnetwork. One or more information databases is provided for storinguser-related information relating to the user behavior. The system mayalso include an analyzer associated with the one or more databases andat least one of the recommendation engine or the question router forreceiving and analyzing the user-related information relating to theuser behavior and extracting or deriving key words therefrom for use incharacterizing user interests, expertise, and skills. In addition, aprofiles database is provided which is associated with at least one ofthe recommendation engine or the question router for storing the keywords as the implicit profile.

The system also includes the additional features of the correspondingmethods set forth above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of components of an example embodiment of thepresent invention relating to the collection and tagging of user profiledata;

FIG. 2 is a block diagram illustrating example fields of an explicituser profile and example tags relating thereto in accordance with thepresent invention;

FIG. 3 is a block diagram of components of an example embodiment of thepresent invention relating to the search for peers;

FIG. 4 is a flowchart of an example embodiment of the peer relevancyalgorithm in accordance with the present invention;

FIG. 5 is a block diagram of components of an example embodiment of thepresent invention relating to the peer connection algorithm;

FIG. 6 is a flowchart of an example embodiment of the peer connectionalgorithm in accordance with the present invention;

FIG. 7 is a block diagram of components of an example embodiment of thepeer forum system of the present invention;

FIG. 8 is a flowchart of an example embodiment of a question routingalgorithm of the question router in accordance with the presentinvention;

FIG. 9 is a block diagram of an example embodiment of a system forcreating an implicit profile in accordance with the present invention;and

FIG. 10 is a flow diagram illustrating an example embodiment of thecreation of an implicit profile in accordance with the presentinvention.

DETAILED DESCRIPTION

The ensuing detailed description provides exemplary embodiments only,and is not intended to limit the scope, applicability, or configurationof the invention. Rather, the ensuing detailed description of theexemplary embodiments will provide those skilled in the art with anenabling description for implementing an embodiment of the invention. Itshould be understood that various changes may be made in the functionand arrangement of elements without departing from the spirit and scopeof the invention as set forth in the appended claims.

In order to use the peer directory of the present invention, a useropts-in to the directory service via a user interface. The directory canreside on a server which is accessible via a network. Once the user isconnected to the server, an explicit user profile can be created,accessed and/or updated. The explicit profile includes, for example,information relating to the product and/or vendor expertise of the user.

Once an explicit profile is complete, a user can then use the inventivesystem to search the peer directory for peers with relevant knowledge.Once suitable peers are found, a peer connection algorithm is used toinitiate a connection to an identified peer through a network, such asvia email or the like. The connection may be made in an anonymousmanner, through an intermediary. Bilateral consent to connect may berequired, via the intermediary, prior to establishing communicationbetween the user and the relevant peer(s).

FIG. 1 illustrates, in block diagram form, the main components of theinventive system that handle the collection and tagging of profile data.A user 10 fills in a profile template using a user interface 11. Theuser interface can comprise, for example, a graphic user interface (GUI)of a type well known in the art. A computer processor residing in aserver 12 (“digital computer”) generates the template for the user tocomplete. The template can request, for example, demographicinformation, information about the user's employer and industry(“firmographic” data), information about the user's education,expertise, professional interests and the like (“about me” data),information identifying what the user is working on at his or her job,information about products and services of interest to the user, andother categories of information.

The user's responses to the template are used to create an explicitprofile for the user. The explicit profile for the user is stored,together with the explicit profiles of other system users, in a profilesdatabase 14, which can be maintained in a memory associated with theserver 12. A search engine (e.g., hardware, firmware, and/or software)resident in server 16 maps the profile data for the user with metadatatags useful for searching the data. The tagged data is then stored in apeer profiles search index 16. The search index 16 can be implemented inanother server or computer accessible to the server 12.

FIG. 2 illustrates examples of the data that can be requested by thetemplate for creating the explicit profiles, as well as the tags thatare provided for this data and stored in the peer profiles search index.As noted above, after entry via the user interface in response to thetemplate, the user data (“explicit profile”) is stored together with theprofiles of other system users in the profiles database 14. The storeddata 20 includes, for example, demographic information for the user,including name, email address, job function, job level, role beingserved at job, and potentially other job related information. Alsoincluded in the stored data 20 is “firmographic” information relating tothe user's employer, such as the firm name, industry, firm size, and thelike. Another category of information included in the data 20 is “aboutme” information, including, for example, the user's areas ofprofessional interest, challenges that the user would like to discusswith peers, a brief biography of the user, and similar data. A “what amI working on” category can include items such as the user's datamanagement and integration projects, web application development andmanagement responsibilities, and the like.

Another category of information that can be maintained for a user in theprofiles database relates to products and services of interest to thatuser. For example, a user may be responsible for specifying, procuringand/or maintaining a business process management (BPM) suite and/or anenterprise search platform provided by a specific vendor, such as theOracle Aqualogic suite or the Vivisimo Velocity search platform. Thiscan be identified in the user's profile, together with pertinentinformation such as the vendor name, the user's involvement with theproduct, the primary operating system on which the suite is run and theuser's recommendation for the product. Other categories of informationcan also be provided in the user's profile that will be useful in thesearch for a peer to assist the user in completing an assigned project.

The information in each explicit profile maintained in the profilesdatabase 14 is transferred to a search engine (e.g., resident in server16) that appends tag profile information to the explicit profile data.The tagged data 22 is then stored in the peer profiles search index 16.In this manner, the search engine can search the tags stored in the peerprofiles search index rather than searching all of the explicit profileinformation itself in the profiles database. This design allows for muchmore efficient searching, higher relevancy and a quicker response when arequester queries the system for peer matches.

The profiles database 14 may also store implicit profiles for each userwhich are created based on an analysis of the user's behavior on thesystem or electronic devices used to access the system, as discussed indetail below in connection with FIGS. 9 and 10. The data in the implicitprofile, once entered in the peer profiles search index 16, may betreated in the same manner as the explicit profile data discussed above(for example, be stored with corresponding tag data) or as discussedfurther below.

FIG. 3 is a block diagram that illustrates a preferred embodiment of thepeer search system. A user (“requester”) 10 uses the user interface 11(FIG. 1) to search for peers via the application implemented in server12 which in turn connects with the search engine in server 16. Thesearch engine can comprise software and/or hardware resident in theserver 16. A peer relevancy algorithm 30 is a key component of thesearch engine, and is described in greater detail in the flowchart ofFIG. 4.

When a user requests to be matched with potential peers via the userinterface, the search engine searches the peer profiles search index 16using the peer relevancy algorithm. Matches are located by the peerrelevancy algorithm based on the tags stored in the peer profiles searchindex and their values, and a list of suitable peers is returned to theapplication at server 12. Server 12 then passes the matched peers to theuser 10 via the user interface. In a preferred embodiment, the peermatches are displayed to the user via a computer display. The userinterface allows the user to view each of the peer matches and to drilldown for further information relating to each peer match. Afterreviewing the peer matches in this manner, the user can decide whichmatch(es) would potentially be most helpful, and commence a procedurefor contacting each such match.

The flowchart of FIG. 4 provides details on the matching and sorting ofsuitable peers on the search engine using tags and their values. Theuser 10 can commence a search for peers having profiles within the peerprofiles search index in server 16 using keywords, as indicated at box40. The keywords entered are used by the search engine to search acrosspeer profile tags stored in the peer profiles search index 16, asindicated at box 41. The search engine finds candidate peers that matchthe search criteria (box 42) and begins assigning weights to thedifferent candidate peers based on which ones have the best matches tothe requester's needs in different categories. In particular, at box 43,a first weight “A” is assigned to candidate peers that have a bestinitiative match with the requester. The “initiative” can be, forexample, a project or venture that the user is currently working on foran enterprise such as an employer.

At box 44, a second weight “B” is assigned to candidate peers who have abest vendor/product match with the requester. At box 45, third weight“C” is assigned to candidate peers who have a best primary operatingsystem (OS) match (e.g., Windows, Mac OS X, SunOS, Linux, Unix, etc.)with the requester. A fourth weight “D” is assigned to candidate peerswho have a best industry match with the requester, as indicated at box46. At box 47, a fifth weight “E” is assigned to candidate peers whohave a best firm size match (e.g., size of employer by number ofemployees, sales revenue, etc.) with the requester. Once all of theweights are assigned, they are summed across all tags based on matchesof the keyword across the tags (box 48).

It should be appreciated that the categories of information to whichweights are assigned at boxes 43-47 are not the only categories forwhich such weights can be assigned. Different categories of informationcan be added to or substituted for those shown, as will be apparent tothose skilled in the art. Moreover, the system is flexible to changeand/or add weights based on the needs of the business using the peersearch system of the invention. In the illustrated embodiment, as shownat box 35 of FIG. 4, weight A=B, and weight C>D>E. For example,numerical weights can be assigned as follows: A=10, B=10, C=5, D=3, andE=2. As these are just examples, the weights actually assigned in aparticular system may be different. Moreover, the system can beconfigurable to assign different weights to different categories asneeded.

As an example of the weighting process, assume that a peer has thefollowing explicit profile:

-   -   Initiative: Application Architecture    -   Current Status: Active    -   Description: PANAMA—fully redundant, zero downtime architecture.    -   Initiative: Data Management & Integration    -   Vendor Name: SampleX Corporation    -   Current Status: New    -   Description: Integration of CorporationA and CorporationB.com        site    -   Initiative: Web Application Development & Management|Edit|Remove    -   Vendor Name: ExampleZ, Inc    -   Current Status: Fully Implemented    -   Description: Implemented the CorporationB Search feature using        ExampleZ Search Engine.    -   Product: SampleX Liquidlogic    -   Vendor Name: SampleX Corporation    -   Product/Service Category: Application Integration and Middleware        Software    -   Your Involvement: Planning and Selection, Negotiation,        Implementation,    -   Maintenance/Support    -   Primary Operating System: Red Hat Linux (Server)    -   Recommendation: Very Likely    -   Product: Windspeed    -   Vendor Name: ExampleZ, Inc    -   Product/Service Category: Search and Information Access    -   Your Involvement: Planning and Selection, Negotiation,        Implementation, Maintenance/Support    -   Primary Operating System: Red Hat Linux (Server)    -   Recommendation: Confidential    -   Comments: Full Life Cycle Implementation with Corporation B.com        application

When a user types in a keyword to search for peers the system will tryto match on the Initiative, Vendor Name, Description, Primary OperatingSystem, Product/Service Category, Product fields (a/k/a tags), Comments,etc. across all peers. Depending on where the match occurs a differentweight might be given. For example, if a user types in the keyword“Application” matches will result on:

-   -   Initiative: Application Architecture—assign a weight of 10    -   Initiative: Web Application Development and Management, assign a        weight of 10    -   Product/Service Category: Application Integration and Middleware        Software, assign a weight of 5    -   Comments: Full Life Cycle Implementation with CorporationB.com        application, assign a weight of 1

All the weights are then summed to provide a unique score for each peer.

Once the weighting process is complete, each candidate peer will have aparticular composite weight (the peer's “score”), and the peers are thensorted based on the composite weights as indicated at box 49. The sortedlist of peers can then be presented to the requester. However, beforepresenting the list of peers to the requester, another series of stepscan be provided to further increase the likelihood that a suitable matchwill be found.

Specifically, some users who have a good past connection history withpeers may be more inclined to respond to a match request than others.The system can therefore keep track of the past history of users inresponding to requests to connect to another user using the system. Withthis information, the system can provide a pre-defined negative bias tousers that have poor connection responses, as indicated at box 52, andprovide a pre-defined positive bias to users who have good pastconnection responses, as indicated at box 54. The bias can beimplemented by simply increasing the weight assigned to good pastresponders and by decreasing the weight assigned to poor pastresponders. Such a bias can be added to or subtracted from the currentweight for a given peer based on a fixed “bias” value or a percentagemodification of the current weighting for each peer match. The bias foreach peer match can then be presented to the requester using a flag orother indicia when the match is presented to the requester (e.g., via acomputer display associated with the user interface) or by re-sortingthe list of peer matches to account for the modified weight resultingfrom the bias. Alternatively, the sorting step 49 can be done subsequentto the bias steps 52 and 54, instead of prior to step 52 as shown inFIG. 4.

After the list of peer matches has been sorted, it is presented to therequester 10 using, e.g., a computer display or the like, as indicatedat box 56. The requester can also use the user interface to view and/orfilter proposed matches based on the tags as indicated at box 58. Suchfiltering can be done, for example, with respect to the requester's(and/or the peers′) industry, firm size, country, job role, vendor,product service/category, etc. The requester can also filter for peersin his own company if he so chooses.

FIG. 5 is a block diagram illustrating the connection of a requester toa peer. After going through the peer search process and receiving a listof the best matched peers, as described in connection with FIG. 4, therequester 10 can request connection to one or more peers that have beenidentified as potentially suitable matches. The request for a connectionmay be made to the server 12 via the user interface 11 (shown in FIG.1). A peer connection algorithm 15, described in greater detail in FIG.6, is associated with the server 12 in order to make a connectionrequest to a particular peer 50 identified by the requester. Theconnection requests may be made using a progressive disclosuremethodology in accordance with the invention, in which the respectiveparties (peer and requester) only learn of the other's identity aftercertain requirements have been met.

As indicated in FIG. 6, the requester 10 first uses the user interfaceto find a potential peer to contact, as indicated at box 60. A requestfor a connection to that peer is then made, again via the user interfaceas indicated at box 61. The peer connection algorithm 15 (FIG. 5) thensends an email (or other electronic message including but not limited totext, instant message, social media message, or the like) to therecipient peer indicating that someone wants to contact the recipientand disclosing various information about the requester such as, forexample, the requester's company, industry, role in the company/industryand a personal message from the requester. The requester's personalinformation, such as name, email address, etc. may or may not bedisclosed at this time.

Upon receipt of the message, the recipient peer 50 reviews theconnection request using a provided user interface, as indicated at box62. If the recipient decides not to accept the request for a connectionwith the requester (box 63), a connection rejection is sent as indicatedat box 67. This rejection can comprise a message sent to the requesterthat the connection has been refused. The system can keep a record tonote that the recipient peer has rejected a communication, which recordcan be used to provide a corresponding bias with respect to thatrecipient peer (as described in connection with box 52 of FIG. 4) shouldthat recipient continue to refuse connections when contacted.

If the recipient peer 50 accepts the request for a connection, aconnection acceptance is sent to the requester 10, as indicated at box64. The acceptance can comprise a message sent to the requesterindicating that the connection has been accepted. A record can be keptby the system regarding the acceptance by the particular recipient peer,for future use in providing a corresponding bias as described inconnection with box 54 of FIG. 4.

Upon acceptance of the connection request by the recipient, anintroductory message is sent by the application on server 12 to both therecipient and requester with the contact information of both parties.Alternatively, the requester can also review the connection status (box65) and obtain contact information of the recipient peer via the userinterface (box 66). At this point, the requester can directly contactthe recipient peer to commence a business relationship. For example, therequester can ask the recipient peer to provide advice and/or assistancein a particular technology or subject area, or to collaborate on aproject that the requester is working on. In one embodiment of thesystem, the recipient peer 50 will be able to obtain contact informationfor the requester via his user interface, as indicated at box 68, assoon as the connection has been accepted by the recipient peer.

FIG. 7 is a block diagram illustrating a peer forum system of thepresent invention. The peer forum system is designed to make askingquestions and answering questions easy and quick. In a preferredembodiment, the process is automated and resident on the server 12, andthere is no substantial human intervention. In the peer forum system,clients 71 utilize a user interface to access online forums that arerestricted to a qualified set of individuals. The clients 71 register,provide profile information, and login to access the forums. The clientscan then ask questions and obtain answers from a highly qualified set ofindividuals, namely peer experts 81. The peer experts answer questionswhen they visit the peer forums. The peer forum system also consists ofthe question router 72, which operates via a question router algorithm90. The question router helps to automatically route the questions torelevant peers. Routing the questions automatically helps the forumsystem to increase the question and answer rates without any manualintervention by the community managers.

FIG. 8 is a flowchart illustrating the operation of the question routeralgorithm 90. The client 71 accesses the question router algorithm 90through the question router 72. Questions are input, as indicated at box73, into a forums database that captures all the questions input byclients. Also, answers to questions are input by peer experts 81 intothe forums database. At any given point in time, questions or answerscan be looked up in the forums database.

Open questions in the forums database are then received into a stagedatabase, as indicated at box 74. Open questions are questions for whichthere have been no answers or replies. A reply may not constitute ananswer and may simply be a request for additional information.Unanswered questions from the forums database are received into thestage database on a periodic basis, for example, every few minutes,every few hours, or every few days. It is also possible to pull allquestions (open or not) and all answers and replies on a periodic basis.

The open questions from the stage database 74 are then received into apeer search module, as indicated at 75, to find peer experts 81 (users)who can answer the questions. The peer search module is operativelyassociated with a recommendation engine, which recommends peers, asindicated at box 76, from the peer search module who can best answer thequestions. This is accomplished e.g., using a combination ofcollaborative and cluster filtering algorithms. The recommendationengine takes into consideration both the explicit and implicit profilesof a peer to figure out the peer's subject matter expertise. Therecommendation engine also takes into account the propensity to answerquestions on the peer forum systems and the subject matter expertise thepeers demonstrate on the peer forum system. If the peer's subject matterexpertise is the same as the open question, then the peer becomes acandidate to answer the open question.

Input is provided to the recommendation engine via a profile database,as indicated at box 77, so that qualified peer matches can be found. Theprofiles database stores an explicit and an implicit profile of the peerexpert 81. An explicit profile comprises information that generallydefines the peer expert 81 based on his own input. This is usuallyderived from registration forms where the peer expert 81 has input hisindustry experience, job titles and duty descriptions, size of company,company name, projects he is working on, vendors he is working with,etc., as discussed in detail above in connection with FIG. 2. Theimplicit profile of the peer expert 81 is based on the user's trackedbehavior on one or more electronic devices and/or on a web site throughwhich the present system is accessed. The implicit profile is describedin detail below in connection with FIG. 9.

After receiving open questions into the peer search module and receivingthe peer recommendation from the recommendation engine, a determinationis then made (box 78) using rules regarding whether or not a candidatepeer expert 81 recommended and entered into the peer search module canbe sent an electronic message encouraging the peer expert 81 to answerthe open questions. There might be rules that limit the number ofmessages that a peer expert 81 can receive, such as “only send threeemails per person per week.” The rules can include any number ofcriteria such as whether a peer expert is likely to answer questions, apeer has unsubscribed to these messages, whether a peer expert is in thesame industry, etc. The peer experts 81 who can get past these rulesbecome eligible to be sent messages, requesting them to answer thequestions. If a peer expert 81 cannot get past the rules, the next bestpeer expert is selected for answering the questions. The message can besent to one or more peer experts as per the configuration of the system.

If a peer expert 81 can pass the rules, a message delivery system, forexample an email delivery system, sends an email, as indicated at box79, to the peer expert 81 requesting him to answer the open questions.The email delivery system consists of email templates and email sendingsystems. A touch database captures all the sent emails sent to the peerexperts 81, as indicated in box 80. The data from the touch database andindicative of the emails sent to the peer experts 81 will be used in thefuture to determine the number of emails sent to peer experts 81 and toadjust the rules.

Peer experts 81 open the emails they receive from the email deliverysystem and find questions they can answer. In a preferred embodiment,these emails include links which will take the peer experts 81 to theopen question where they can comment on or answer the question. Althoughthe example embodiment of the invention is described in connection withemail messaging, other electronic messaging systems may be used with thepresent invention, including but not limited to text messaging, instantmessaging, social media messaging, and the like.

The interactive peer directory system enables professionals to findsuitable peers for assistance with, advice on, and/or collaboration on aparticular project. Although the peers are generally people that workfor other companies or are independent consultants, academics, or thelike, they can also be employed by the same company as the requester.

As discussed above in connection with FIG. 1, a user interface enablesthe explicit user profile information to be entered and stored in aprofiles database 14. A search engine appends tags to the user profileinformation. A search index 16 associated with the search engine storestagged user profile information in an indexed form. Implicit profileinformation may also be stored in profiles database 14 and tagged andindexed in the peers profiles search index 16 in the same manner as theexplicit profile information is stored, tagged and indexed. A peerrelevancy algorithm associated with the search engine searches forcandidate peers among the indexed user profile information stored in thesearch index. The peer relevancy algorithm assigns weights to candidatepeers based on different categories of the indexed user profileinformation, and selects peer matches based on the assigned weights.

Once the system provides one or more potential peer matches to therequester, the requester can initiate a connection request to a selectedpeer. If the selected peer accepts the connection request, the requestercan contact the peer directly. In order to provide matches that are mostlikely to accept a connection request, the system can keep track ofwhich candidate peers have a history of accepting requests to connectand which have a history of refusing to connect. The list of potentialmatches provided to the requester can be biased to favor those that havea tendency to accept connection requests.

Moreover, question and answer rates can be increased in accordance withthe invention to maintain a dynamic and healthy community of users. Toachieve this, open questions are pulled from forums and a recommendationengine is used to find peer experts to answer the open questions. Emailsor notifications are then sent to the peer experts to solicit theirresponse to the questions.

In a further example embodiment of the present invention, methods andsystems for creating an implicit profile for use by a recommendationengine or a question router 112 are provided. FIG. 9 shows a blockdiagram of an example embodiment of an automated system for creating animplicit profile for use by at least one of a recommendation engine or aquestion router 112. Each user may employ one or more electronic devices100. A tracking application 102 may be provided on each electronicdevice 100 (e.g., as a downloadable application). It should also beappreciated that the tracking application may alternatively oradditionally comprise a software application integral to the webinterface of the question router or recommendation engine 112, a cloudbased application, or a web-based tracking application residing on thenetwork 104 or other component of the system. The tracking application102 may track user behavior on at least one of the one or moreelectronic devices 100 and on an electronic communications network 104.One or more information databases 106 may be provided for storinguser-related information relating to the user behavior. An analyzer 110may be provided for analyzing the user-related information relating tothe user behavior received or obtained from the one or more databases106 in order to extract or derive key words therefrom which are used tocharacterize user interests, expertise, and skills. A profiles database114 is provided for storing the key words as the implicit profile 116.The key words are used by the recommendation engine or question router112 when matching a request from a querying user to a potential user orgroup of users having the relevant background to respond to the request.

The information databases 106, analyzer 110, and profiles database 114are associated with the recommendation engine/question router 112. FIG.9 shows the profiles database 114 as part of the recommendationengine/question router 112. However, those skilled in the art willappreciate that the profiles database 114 may be implemented separatelyfrom the recommendation engine/question router 112. Similarly, theanalyzer 110 and the information databases 106 may be separate from therecommendation engine/question router 112 as shown in FIG. 9, orintegrated into the recommendation engine/question router 112. Further,the question router and recommendation engine 112 may be integrated onthe same or separate hardware and software platforms and for ease ofexplanation are illustrated herein as a common device/softwareapplication 112.

The user-related information may comprise at least one of search termsused, documents read, documents opened, documents printed, documentssaved, documents created, documents edited, documents commented on,annotations entered on documents, highlighted terms in documents,websites visited, webpages viewed, Internet searches conducted, ratingsprovided by the user on documents, products or services, user-createdproduct or service reviews, multi-media items played, social forumthreads opened, social forum threads participated in, people profilesopened, user items shared on the electronic communications network,shared items of others on the electronic communications network accessedby the user, user created content, online events, seminars, trainingcourses, or webinars attended, in-person or online events, seminars, ortraining courses registered for, news or information feeds set up on theelectronic communications network, emails written, emails received,blogs read, blog posts entered, software applications installed,computer hardware installed, software updates downloaded, one or more ofalerts, follows, and likes set up on the electronic communicationsnetwork, and similar information.

In addition, the user-related information may be obtained from useractivity on a web site used to access the recommendation engine or thequestion router 112. For example, the user-related information maycomprise at least one of questions answered, answers provided,information and materials reviewed in answering questions, number ofquestions answered in a subject area, percentage of questions acceptedfor response, subject matter expertise based on answers submitted,number of times recommended as an expert in a subject area, userfrequency of logging in to the web site, user preference for type ofdigital network, user preference for type of electronic device, userpreferred information channels including one or more of reading orprinting of documents, use of multimedia, and interacting socially inforums, user open and click-through rates for relevant emails, andsimilar information.

In addition, the user-related information may comprise at least one ofuser requests or user responses via the web site used to access therecommendation engine or the question router 112.

Optionally, the user-related information may also comprise user requestssubmitted to the recommendation engine or the question router 112 viaonline forms, emails, or computer applications that are recorded in arequest database 122 to find at least one of experts, analysts, orpeers, or to receive requested information or materials.

Further, the user-related information may comprise user requestssubmitted to the recommendation engine or the question router 112 orother linked web-based tools via online forms, emails, or computerapplications that are recorded in a request database 122 for at leastone of vendor proposals, product demonstrations, price quotes, and thelike.

The user-related information may also be obtained from electronicimprints from user interaction with the one or more electronic devices100 that are logged in a database 106. The one or more electronicdevices 100 may comprise at least one of a computer, a tablet computer,a laptop, a smartphone, an Internet enabled device, and the like.

The electronic imprints may be created by at least one of using a webinterface, using a web browser, using a mobile application, using acomputer application or program, sending or receiving an email, loggingor recording a telephone call or voice message, manipulation of aself-reporting electronic system, downloading or installing one or moreof programs, applications, documents, multimedia content, music, andsoftware updates, and the like. Key words may be derived from theuser-related information obtained from the electronic imprints.

The tracking by the tracking application 102 may comprise at least oneof storing data from the user behavior at the time of the user behavior,searching the one or more electronic devices 100 for data relating tothe user behavior, recording user interaction on the one or moreelectronic devices 100, and the like.

The tracking application 102 may be a crawling or searching applicationrunning on the one or more electronic devices 100 for searching variouscomputer applications or storage locations on the one or more electronicdevices 100 for obtaining the user-related information. The tracking maybe a continuous process (e.g., enabled by an application runningcontinuously on one or more of the electronic device(s) 100, the cloud,the web interface, the network, and or at the recommendationengine/question router 112, or other suitable location). Alternatively,the tracking may be periodic (e.g., the tracking application, whereverlocated, may be set to search the electronic device(s) 100 or thenetwork 104 at configurable intervals). The tracking application 102 mayalso be configurable to automatically search the electronic device 100when certain events occur, including but not limited to upon startup ofthe device 100, upon sensing user interaction with the device 100, orupon sensing a configuration change to the device 100, upon the sendingor receiving of information, and the like.

The various computer applications may comprise one or more of a wordprocessing application, a web browser, an electronic calendar, an emailprogram, spreadsheet applications, social media applications, messagingapplications, content editing, highlighting, and annotating programs,and the like.

The storage locations may comprise at least one of hard drive locations,file folders, document folders, web browser cookie folders, emailfolders, databases, spreadsheet folders, shared folders, networkedfolders, music folders, software application folders, media files, filedirectories, social directories, activity logs, and the like.

FIG. 10 illustrates an example embodiment of a process for creating theimplicit profile 116 for a user 101. For example, the user 101 may usean electronic device 100 to interact with a computer web interface 201,use a mobile interface 301 (e.g., on a smartphone, tablet computer orother portable electronic device), send a communication or otherwiseexchange information from an electronic device 401, or take anadministrative action with regard to the electronic device 501, or thelike. The tracking application 102 (whether resident on the electronicdevice 100, within the web interface 201, or cloud or network-based)tracks the user actions and inputs. Any user-related information or dataconsumed 202, any interactions 302, communications 402, oradministrative actions 502 carried out by the user on the electronicdevice(s) 100 may be respectively stored in an item database 106 a, aninteraction database 106 b, a form database 106 c, and an inventorydatabase 106 d (all of which may constitute the information databases106).

The analyzer 110 obtains and analyzes the user-related information fromthe various information databases 106, 106 a, 106 b, 106 c, and/or 106d. The key words may be extracted or derived from the user-relatedinformation by the analyzer 110 by applying at least one of lexicalanalysis 601, metadata analysis 602, natural language processinganalysis 603, or similar processing techniques or combinations thereof.The extracted key words 606 are then stored in the profiles database 114as an implicit profile 116.

In one example embodiment, weightings 608 may be assigned to the keywords by the analyzer 110 and stored with each of the key words in theprofiles database 114. The weightings 608 may be assigned based on oneor more of relevancy calculations of the user-related information, anestimate of accuracy of the user-related information, type of theuser-related information, source of the user-related information, amountof each type of the user-related information, time spent by user on eachtype or item of user-related information, recency of the user-relatedinformation, relation of the user-related information to current keywords in the implicit profile, relation of the user-related informationto information in an explicit profile of the user, and the like.

Optionally, separate recency scores 610 may be assigned by the analyzer110 and stored with the key words in the profiles database 114indicating the relative age of the key words. For example, key wordsobtained or derived from the same set of user-information will beassigned the same recency score.

The method may further comprise storing an explicit profile 118 for theuser 101 in the profiles database 114 together with the implicit profile116 for use by at least one of the recommendation engine and thequestion router 112. The explicit profile 118 may comprise profile dataobtained by direct input from the user 101 as described above inconnection with FIGS. 1 and 2.

A plurality of the implicit and explicit profiles 116, 118 may be storedfor each of a corresponding plurality of respective users 101 in theprofiles database 114. The implicit profile 116 of the user 101 may bematched with the explicit profile 118 of the user 101 for use inprocessing recommendation requests or information requests by therecommendation engine or question router 112.

Optionally, the key words, values, key word weightings, and otherinformation stored in the matching explicit and implicit profiles 116,118 of the user may be merged to create a merged profile 120 for use byat least one of the recommendation engine and the question router 112.The merged profile 120 may contain key words from both the implicitprofile 116 and from the explicit profile 118, with correspondingweightings and (optionally) recency scores. The weightings and/orrecency scores of the key words in the merged profile may be adjustedduring the merging process. For example, key words present in bothprofiles 116 and 118 may have weightings adjusted upwards to reflecthigher relevance, and where the same key words are present in both theimplicit and explicit profiles, the more current recency score ismaintained while the older recency score is deleted for those key words.

The question router 112 may be adapted to automatically route a questionfrom a querying user to one or more of the other users on the electroniccommunications network 104 based on a matching of key words obtainedfrom the question with at least the implicit profiles 116 of the one ormore other users.

The recommendation engine 112 may be adapted to accept a recommendationrequest for an expert from a querying user and recommending one or moreof the other users on the electronic communications network 104 as anexpert based on a matching of key words obtained from the recommendationrequest with at least the implicit profiles 116 of the one or more otherusers.

The method may also comprise providing user feedback on relevancy of atleast one of a recommendation request from the recommendation engine, aresponse to the recommendation request from a recommended peer, aquestion from the question router, and a response to the question fromthe recommended peer to the user's expertise, and storing of thefeedback. The user's feedback on relevancy may be used to adjust keyword weightings 608 in the implicit profile 116 of the user and/or therecommended peer. The feedback may be provided via the electronic device100 to the recommendation engine/question router 112 via the network 104and stored at either a dedicated feedback database, or in a designatedlocation in one of the information databases 106 or the requestsdatabase 122.

It should now be appreciated that the present invention providesadvantageous methods and apparatus for creating an implicit profile foruse by a recommendation engine or question router, resulting in moretargeted recommendations and responses to user queries.

Although the invention has been described in accordance with variousexample embodiments, various additional embodiments can be provided andare intended to be included within the scope of the claims.

What is claimed is:
 1. An automated method for creating an implicitprofile for use by at least one of a recommendation engine or a questionrouter, comprising: tracking user behavior on at least one of one ormore electronic devices and an electronic communications network;analyzing user-related information relating to the user behavior toextract or derive key words therefrom which are used to characterizeuser interests, expertise, and skills; and storing the key words in aprofiles database as the implicit profile.
 2. A method in accordancewith claim 1, wherein the user-related information comprises at leastone of search terms used, documents read, documents opened, documentsprinted, documents saved, documents created, documents edited, documentscommented on, annotations entered on documents, highlighted terms indocuments, websites visited, webpages viewed, Internet searchesconducted, ratings provided by the user on documents, products orservices, user-created product or service reviews, multi-media itemsplayed, social forum threads opened, social forum threads participatedin, people profiles opened, user items shared on the electroniccommunications network, shared items of others on the electroniccommunications network accessed by the user, user created content,online events, seminars, training courses, or webinars attended,in-person or online events, seminars, or training courses registeredfor, news or information feeds set up on the electronic communicationsnetwork, emails written, emails received, blogs read, blog postsentered, software applications installed, computer hardware installed,software updates downloaded, and one or more of alerts, follows, andlikes set up on the electronic communications network.
 3. A method inaccordance with claim 1, wherein: the user-related information isobtained from user activity on a web site used to access therecommendation engine or the question router; the user-relatedinformation comprises at least one of questions answered, answersprovided, information and materials reviewed in answering questions,number of questions answered in a subject area, percentage of questionsaccepted for response, subject matter expertise based on answerssubmitted, number of times recommended as an expert in a subject area,user frequency of logging in to the web site, user preference for typeof digital network, user preference for type of electronic device, userpreferred information channels including one or more of reading orprinting of documents, use of multimedia, and interacting socially inforums, and user open and click-through rates for relevant emails.
 4. Amethod in accordance with claim 3, wherein the user-related informationcomprises at least one of user requests or user responses via the website used to access the recommendation engine or the question router. 5.A method in accordance with claim 1, wherein the user-relatedinformation comprises user requests submitted to the recommendationengine or the question router via online forms, emails, or computerapplications that are recorded in a request database to find at leastone of experts, analysts, or peers, or to receive requested informationor materials.
 6. A method in accordance with claim 1, wherein theuser-related information comprises user requests submitted to therecommendation engine or the question router or other linked web-basedtools via online forms, emails, or computer applications that arerecorded in a request database for at least one of vendor proposals,product demonstrations, and price quotes.
 7. A method in accordance withclaim 1, wherein the user-related information is obtained fromelectronic imprints from user interaction with the one or moreelectronic devices that are logged in a database.
 8. A method inaccordance with claim 7, wherein the one or more electronic devicescomprise at least one of a computer, a tablet computer, a laptop, asmartphone, and an Internet enabled device.
 9. A method in accordancewith claim 7, wherein the electronic imprints are created by at leastone of using a web interface, using a web browser, using a mobileapplication, using a computer application or program, sending orreceiving an email, logging or recording a telephone call or voicemessage, manipulation of a self-reporting electronic system, anddownloading or installing one or more of programs, applications,documents, multimedia content, music, and software updates.
 10. A methodin accordance with claim 1, wherein weightings are assigned and storedwith each of the key words in the profiles database.
 11. A method inaccordance with claim 10, wherein the weightings are assigned based onone or more of relevancy calculations of the user-related information,an estimate of accuracy of the user-related information, type of theuser-related information, source of the user-related information, amountof each type of the user-related information, time spent by user on eachtype or item of user-related information, recency of the user-relatedinformation, relation of the user-related information to current keywords in the implicit profile, relation of the user-related informationto information in an explicit profile of the user.
 12. A method inaccordance with claim 1, wherein the tracking comprises at least one ofstoring data from the user behavior at the time of the user behavior,searching the one or more electronic devices for data relating to theuser behavior, and recording user interaction on the one or moreelectronic devices.
 13. A method in accordance with claim 1, wherein thetracking is enabled via a crawling or searching application running onthe one or more electronic devices for searching various computerapplications or storage locations on the one or more electronic devicesfor obtaining the user-related information.
 14. A method in accordancewith claim 13, wherein the various computer applications comprise one ormore of a word processing application, a web browser, an electroniccalendar, an email program, spreadsheet applications, social mediaapplications, messaging applications, and content editing, highlighting,and annotating programs.
 15. A method in accordance with claim 13,wherein the storage locations comprise at least one of hard drivelocations, file folders, document folders, web browser cookie folders,email folders, databases, spreadsheet folders, shared folders, networkedfolders, music folders, software application folders, media files, filedirectories, social directories, and activity logs.
 16. A method inaccordance with claim 1, wherein the key words are extracted or derivedfrom the user-related information by applying at least one of lexicalanalysis, metadata analysis, and natural language processing analysis.17. A method in accordance with claim 1, further comprising: storing anexplicit profile for the user in the profiles database together with theimplicit profile for use by at least one of the recommendation engineand the question router, the explicit profile comprising profile dataobtained by direct input from the user.
 18. A method in accordance withclaim 17, wherein a plurality of the implicit and explicit profiles arestored for each of a corresponding plurality of respective users in theprofiles database.
 19. A method in accordance with claim 18, wherein theimplicit profile of the user is matched with the explicit profile of theuser for use in processing recommendation requests or informationrequests.
 20. A method in accordance with claim 19, wherein the keywords, values, key word weightings, and other information stored in thematching explicit and implicit profiles of the user are merged to createa merged profile for use by at least one of the recommendation engineand the question router.
 21. A method in accordance with claim 18,wherein the question router is adapted to automatically route a questionfrom a querying user to one or more of the other users on the electroniccommunications network based on a matching of key words obtained fromthe question with at least the implicit profiles of the one or moreother users.
 22. A method in accordance with claim 18, wherein therecommendation engine is adapted to accept a recommendation request foran expert from a querying user and recommending one or more of the otherusers on the electronic communications network as an expert based on amatching of key words obtained from the recommendation request with atleast the implicit profiles of the one or more other users.
 23. A methodin accordance with claim 18, further comprising: providing user feedbackon relevancy of at least one of a recommendation request from therecommendation engine, a response to the recommendation request from arecommended peer, a question from the question router, and a response tothe question to the user's expertise, and storing the feedback.
 24. Amethod in accordance with claim 23, where the user's feedback onrelevancy is used to adjust key word weightings in the implicit profileof at least one of the user or the recommended peer.
 25. A system forcreating an implicit profile for use by at least one of a recommendationengine or a question router, comprising: an electronic communicationsnetwork; one or more electronic devices for each user in communicationwith the electronic communications network; a software applicationrunning on each of the electronic devices adapted for tracking userbehavior on at least one of the one or more electronic devices and theelectronic communications network; one or more information databases forstoring user-related information relating to the user behavior; ananalyzer associated with the one or more databases and at least one ofthe recommendation engine or the question router for receiving andanalyzing the user-related information relating to the user behavior andextracting or deriving key words therefrom for use in characterizinguser interests, expertise, and skills; and a profiles databaseassociated with at least one of the recommendation engine or thequestion router for storing the key words as the implicit profile.